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Crime Data Analysis, Visualization and Prediction Using LSTM
Mufeeda Manengadan,
Silpa Nandanan,
Neethu Subash
Issue:
Volume 7, Issue 3, June 2021
Pages:
51-59
Received:
25 January 2021
Accepted:
19 April 2021
Published:
8 May 2021
Abstract: Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted for the purpose of visualization. Big data analytics and visualization techniques were utilized to analyze crime big data within the different parts of India. Here, we have taken all the states of Indian for analysis, visualization and prediction. The series of operations performed are data collection, data pre-processing, visualization and trends prediction, in which LSTM model is used. The data includes different cases of crimes with in different years and the crimes such as crime against women and children in which, kidnap, murder, rape. The predictive results show that the LSTM perform better than neural network models. Hence, the generated outcomes will benefit for police and law enforcement organizations to clearly understand crime issues and that will help them to track activities, predict the similar incidents, and optimize the decision making process.
Abstract: Crimes are common social problems that can even affect the quality of life, even the economic growth of a country. Big Data Analytics (BDA) is used for analyzing and identifying different crime patterns, their relations, and the trends within a large amount of crime data. Here, BDA is applied to criminal data in which, data analysis is conducted fo...
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Developing Genomic Predictive Biomarkers for Survival Benefit from Adjuvant Chemotherapy in Early-Stage Lung Cancer Patients for Personalized Medicine
Issue:
Volume 7, Issue 3, June 2021
Pages:
60-68
Received:
1 February 2021
Accepted:
8 February 2021
Published:
8 May 2021
Abstract: Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. However, at an individual level, not all patients may benefit from ACT. Given the well-known adverse effects and toxicity of ACT, finding the patients that are most likely to benefit from ACT is paramount. Thus, the purpose of this research is to utilize gene expression and clinical data from lung cancer patients to develop a statistical decision support algorithm to find predictive genomic biomarkers and identify subgroups of patients who benefit from ACT. Cox regression models are trained using a randomized controlled trial gene expression data from the National Center for Biotechnology Information (NCBI) utilizing explicit treatment interaction terms. To handle high dimensions inherent in gene expression data, a regularized Cox regression model with lasso penalty is applied to find the most significant interacting markers. Risk scores are estimated from the proposed model and are used to stratify patients into a high risk or low risk group respective to ACT treatment. After applying the model to an independent validation genomic data set, we show that patients who underwent the recommended treatment according to their risk group estimated by our proposed algorithm exhibit a slightly higher survival rate than those who do not.
Abstract: Surgical resection only remains the standard choice for the treatment of early-stage non-small cell lung cancer (NSCLC) patients. Preliminary studies suggest that the application of adjuvant chemotherapy with surgery (ACT) is associated with a better prognosis for more severe NSCLC patients compared to those who only underwent surgical resection. H...
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Application of Error Correction Model in Assessing the Impact of Macroeconomic Variables on Stock Market Performance in Nigeria
Fatoki Olayode,
Adeleye Najeem Friday,
Afolabi Nosimot Omowunmi
Issue:
Volume 7, Issue 3, June 2021
Pages:
69-75
Received:
26 February 2020
Accepted:
18 March 2020
Published:
14 May 2021
Abstract: The objective of this paper is to investigate the relationships between some selected macroeconomic variables and stock market returns in Nigeria. Time series data on macroeconomic variables were collected from Central Bank of Nigeria (CBN) annual statistical bulletin 2018 covering between years 1981 to 2018. The error correction model (ECM) was used to show the strength of relationship between the macroeconomic variables and stock market performance. The result of the coefficients of macroeconomic variables are negative and positive values and also significant and insignificant. Hence, there is disequilibrium in the long run and must be corrected. The coefficient of parameters estimates for short run for return and gross domestic product at lag 1 are positive while values of crude oil prices, interest rate and inflation rate at lag 1 are negative. Hence, there is short run dynamic changes in crude oil prices, interest rate and inflation rate could lead to negative changes in stock market performance. The ECM coefficient is -0.80 suggesting that any disequilibrium can be corrected at the speed or rate of 80 percent within a year. In view of this, there is long run dynamic influence running from macroeconomic variables to stock market performance in Nigeria.
Abstract: The objective of this paper is to investigate the relationships between some selected macroeconomic variables and stock market returns in Nigeria. Time series data on macroeconomic variables were collected from Central Bank of Nigeria (CBN) annual statistical bulletin 2018 covering between years 1981 to 2018. The error correction model (ECM) was us...
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Scientific Data Analysis: Employing Sentimental Analysis to Prove Correlation Between Social Media and Electric Vehicles in Modern Society
Issue:
Volume 7, Issue 3, June 2021
Pages:
76-81
Received:
26 April 2021
Accepted:
12 May 2021
Published:
31 May 2021
Abstract: In recent decades, the development of technology has brought several changes in the global society. Enhanced communication methods enabled rapid dissemination of information, impacting peoples’ decision making and consumption. Moreover, indiscreet production and resource consumption caused environmental damage, hence leading to the advent of electric vehicles in the automotive industry. This research paper delves into the influence of social media on market share and stock prices of electric vehicle manufacturers. Social media plays a significant role in conveying information and therefore influencing consumption. To conduct research, we gathered data – tweets, news articles, EV stock prices, EV market shares, air quality of major cities – to prove correlation between social media and EV stock prices. Market data were mainly used for analysis and prediction, and information regarding air quality was used to explain how electric vehicles could gather huge momentum. We analyzed how electric vehicle market shares have changed in 10 years, and how individual manufacturers, such as Tesla, General Motors, and Hyundai, increased production and sales over time, using data analysis and visualization. By comparing these data with media coverage of electric vehicles using sentimental analysis, we could figure out how social media could impact sales and stock prices of automotive producers. The main driving force of the meteoric rise of electric vehicles was favorable media coverage of electric vehicles. Data collection was done by effective Python tools that could significantly reduce time.
Abstract: In recent decades, the development of technology has brought several changes in the global society. Enhanced communication methods enabled rapid dissemination of information, impacting peoples’ decision making and consumption. Moreover, indiscreet production and resource consumption caused environmental damage, hence leading to the advent of electr...
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Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems
Mohamed Darfoun,
Huda Hosson
Issue:
Volume 7, Issue 3, June 2021
Pages:
82-88
Received:
8 May 2021
Accepted:
7 June 2021
Published:
16 June 2021
Abstract: Integration of Distributed Generations (DGs) in distribution systems receives great attention nowadays due to its numerous benefits, the most important of which are reducing the overall power losses and improving voltage profile in distribution systems. In order to enhance the performance of the network, the DG units must be installed at optimal placement and sizing. Solution techniques for DG placement rely on various optimization methods. In this paper, a hybrid heuristic technique is proposed to solve the optimization problem for a single DG unit using two heuristic tests performed in two stages. In the first stage, a sensitivity test is used to determine the candidate location for DG placement. Then in the second stage, the optimal size is identified using a curve fitting test. A comprehensive analysis is performed in order to validate the results of the proposed technique. Both techniques have been tested on IEEE 33-bus and 69-bus radial distribution test systems. The obtained results show that although the comprehensive analysis can achieve slightly greater power loss reduction and voltage profile improvement, it requires a large number of tests that is proportional to the size of the distribution system. On the other hand, the proposed technique can achieve comparable results using a small fixed number of tests for any system, which means that this technique reduces the solution search space i.e., the computational demand and convergence time, while maintaining satisfactory results.
Abstract: Integration of Distributed Generations (DGs) in distribution systems receives great attention nowadays due to its numerous benefits, the most important of which are reducing the overall power losses and improving voltage profile in distribution systems. In order to enhance the performance of the network, the DG units must be installed at optimal pl...
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Separation of Data Cleansing Concept from EDA
Issue:
Volume 7, Issue 3, June 2021
Pages:
89-97
Received:
25 May 2021
Accepted:
8 June 2021
Published:
22 June 2021
Abstract: Available dataset whether it is structured, semi structured or unstructured data, is used for various purposes. These data sets are mostly used for solving an issue using different kinds of techniques like visualization, descriptive, algorithms etc. This data process includes many levels, two of those steps are exploratory data analysis (EDA) and data cleansing. Data cleansing and exploratory data analysis (EDA) are two major operations of any data mining or machine learning study. After collecting the data from various sources, Data cleansing is done to make the data set more accurate, useful and less redundant. Data cleansing is useful to get the accurate information from the dataset and It is used to deal with null values, duplicate values, multiple values, inconsistent value, inaccurate value etc, Which are existing in that data set and It can make our data set filled with error which also affects the analysis and decision making process. By performing data cleansing, we can get rid of many types of misleadings like getting inaccurate output, inaccurate model of machine learning, not getting the hidden patterns behind that data set etc. The purpose of this paper is to study existing research of Data cleansing and EDA and state why Data cleansing process is not part of exploratory data analysis (EDA).
Abstract: Available dataset whether it is structured, semi structured or unstructured data, is used for various purposes. These data sets are mostly used for solving an issue using different kinds of techniques like visualization, descriptive, algorithms etc. This data process includes many levels, two of those steps are exploratory data analysis (EDA) and d...
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